Emotion 2014, Vol. 14, No. 3, 522–531

© 2014 American Psychological Association 1528-3542/14/$12.00 DOI: 10.1037/a0035960

Coregulation of Respiratory Sinus Arrhythmia in Adult Romantic Partners Jonathan L. Helm

David A. Sbarra

University of California, Davis

University of Arizona

Emilio Ferrer

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University of California, Davis Questions surrounding physiological interdependence in romantic relationships are gaining increased attention in the research literature. One specific form of interdependence, coregulation, can be defined as the bidirectional linkage of oscillating signals within optimal bounds. Conceptual and theoretical work suggests that physiological coregulation should be instantiated in romantic couples. Although these ideas are appealing, the central tenets of most coregulatory models await empirical evaluation. In the current study, we evaluate the covariation of respiratory sinus arrhythmia (RSA) in 32 romantic couples during a series of laboratory tasks using a cross-lagged panel model. During the tasks, men’s and women’s RSA were associated with their partners’ previous RSA responses, and this pattern was stronger for those couples with higher relationship satisfaction. The findings are discussed in terms of their implications for attachment theory, as well as the association between relationships and health. Keywords: coregulation, romantic partners, respiratory sinus arrhythmia, parasympathetic activity

Coan, 2008, 2010; Sbarra & Hazan, 2008). These theories suggest that enhanced regulation operates, at least in part, through physiological synchrony (Field, 1985, 2012), which involves a concordant, dyad-level response to environmental demands. From a conceptual perspective, ideas concerning physiological interdependence have considerable appeal and align closely with animal literature regarding coregulation (Hofer, 1984), but research evaluating the existence of physiological interdependence in adult romantic relationships is scarce (Butler & Randall, 2013). The current study seeks to address this limitation by illuminating one pathway through which this physiological synchrony may operate in romantic couples.

Although it is well-known that high-quality social connections are vital for health and well-being (Holt-Lunstad, Smith, & Layton, 2010; House, Landis & Umberson, 1988; Uchino, 2004), we have only a nascent understanding of the behavioral and physiological mechanisms through which relationships exert their salubrious effects (Kiecolt-Glaser, Gouin, & Hantsoo, 2010; Robles & Kiecolt-Glaser, 2003; Slatcher, 2010). Close relationships may promote health by alleviating psychosocial stress (Cohen, Gottlieb, & Underwood, 2004; Uchino, 2004) or, in some cases, disrupt health by enhancing stress (Gottman, 1994; Robles & KiecoltGlaser, 2003). The exact mechanisms that drive these processes are not clear, but theory offers insight into the different pathways that buffer or potentiate stress. On the dampening of physiological distress, both social baseline and attachment theories define highquality relationships as those in which two people can reliably use each other as regulatory resources to attenuate threat in the environment and enhance appetitive states (Beckes & Coan, 2011;

Close Relationships and Regulation Healthy close relationships promote emotion and self-regulation (Brennan & Shaver, 1995; Simpson, Rholes, & Nelligan, 1992), and these processes are instantiated across multiple channels. Several studies provide evidence for heightened emotion and affect regulation during infancy through adulthood (Diamond, 2001; Diamond & Hicks, 2005). Although these studies supported their conclusions through self-reports, it is likely that an attachment figure directly modulates the underlying physiological responses associated with affect and emotion. In fact, Bowlby’s original ideas surrounding attachment described physiological regulation as the precursor of a secure attachment, such that an individual’s physiological set point for felt security is promoted and maintained through the presence of and interaction with an intimate partner (Bowlby, 1973). Several studies support this perspective (for a review see Diamond & Fagundes, 2010). However, a different, and largely understudied, concept describes another process of regulation in close relationships: coregulation (Butler & Randall, 2013; Sbarra & Hazan, 2008). Models of human coregulation posit a dyad-level interdependence, or synchrony, across multiple physi-

This article was published Online First April 7, 2014. Jonathan L. Helm and Emilio Ferrer, Department of Psychology, University of California, Davis; David A. Sbarra, Department of Psychology, University of Arizona. This research was supported in part by grants from the National Science Foundation (NSF; BCS-05-27766 and BCS-08-27021) and the National Institutes of Health, National Institute on Neurological Disorders and Stroke (R01 NS057146-01) to Emilio Ferrer and in part by grants from the National Institute of Aging (R21 028454), the National Institute of Mental Health (R03 074637), and the NSF (BCS-09-19525) to David A. Sbarra. We appreciate the help by the members of the Dynamics of Dyadic Interactions Project Laboratory at University of California, Davis. Correspondence concerning this article should be addressed to Jonathan L. Helm, Department of Psychology, University of California, One Shields Avenue, Davis, CA 95616. E-mail: [email protected] 522

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ological systems in romantic partners, and this physiological synchrony is hypothesized to be one element— beyond stress buffering alone—that can explain how high-quality relationships may exert positive effects on distal health outcomes (Hazan, Gur-Yaish, & Campa, 2004; Sbarra & Hazan, 2008).

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The Promises and Pitfalls of Physiological Synchrony Extending results from animal research (Hofer, 1984, 1996), Field (1985) described a variety of studies concerning physiological regulation as a function of social interaction (also see Field, 2012). For example, mothers that shift their sensitivity and responses to the needs of their child tend to synchronize their heart rates with their infants’, leading to a decreased heart rate in both (Field, 1977). However, mothers who overstimulate their child also evidence synchrony, but show increases in both heart rates (Field, 2007; Papousek, 2007). Thus, mothers’ behaviors directly modulate children’s arousal, serving to either promote regulation (e.g., attentive responsivity) or inhibit it (e.g., overstimulation). Similar patterns of physiological synchrony and regulation are reported for child peers, preadolescent friends, and family members (Feldstein & Field, 2002; Field et al., 1992; Konvalinka et al., 2011; Montagner, Restoin, & Henry, 1982; Wade, Ellis, & Bohrer, 1973), indicating that the phenomenon is not unique to mother– infant dyads (for review, see Field, 2012). Moreover, physiological synchrony does not reliably occur between acquaintances, suggesting that matched responses are an emergent property of a close relationship and, potentially, one of the indicators of an attachment relationship (Field, 2012; Hazan, Campa, & Gur-Yaish, 2006; Sbarra & Hazan, 2008). Although these findings are provocative, physiological synchrony is operationalized differently throughout the literature such that it has become, essentially, unfalsifiable. Butler and colleagues (Butler, 2011; Butler & Randall, 2013) have noted that this imprecision is hindering progress in the field and have outlined two different processes of synchrony to guide future research (see Figure 1 in Butler & Randall, 2013). The first, called morphostatic, describes a degree of correlation between partners’ responses with stabilization around an optimal set point. For example, partners could use specific tones, facial gestures, or topics of conversation that cause their respective affective states to range between mildly negative to fairly positive. Hence, their behavioral interaction stabilizes their mood around an ideal location. In contrast, morphogenic synchrony also includes a degree of interdependence, but produces stable change in the set point until physiological arousal goes beyond an ideal response. Here, partners could exchange cues (e.g., yelling or complaining) that promote negative mood. Therefore, the behavioral interaction leads to a downward spiral with each partner feeling more and more upset. As suggested above, synchronous processes are not limited to behavior and mood, but are expected to manifest in physiological responses as well (Butler, 2011; Sbarra & Hazan, 2008). For morphostatic synchrony, partners’ behavioral interactions tend to upregulate and downregulate physiology to promote regulation, whereas morphogenic synchrony upregulates or downregulates physiology and leads to dysregulation. Following this nomenclature, sensitive mothers promote morphostatic synchrony because their heart rates vary around a lower average heart rate, whereas overstimulating mothers show morphogenic synchrony because

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their heart rates increase continuously. The special case of morphostatic synchrony is defined as coregulation. Butler and Randall (2013) suggest that partners who coregulate each other’s subjective experience and physiological states receive benefits because they help stabilize each other around an optimal set point, whereas those showing morphogenic synchrony interrupt basic regulatory functions as they lead each other away from this set point. Empirical research concerning behavioral synchrony in romantic relationships supports morphogenic processes for couples with low relationship satisfaction. As they interact, dissatisfied couples reciprocate negative affect (NA) and exhibit identifiable increases in NA (Gottman, 1979; Levenson & Gottman, 1983; Saxbe & Repetti, 2010; Schoebi, 2008). Investigations of physiological variables associated with NA (heart rate, pulse transit time, electrodermal activity, somatic activity, and cortisol) manifest synchrony between partners and overall increases within partners during social interaction (Gottman, 1994; Gottman & Notarius, 2000; Levenson & Gottman, 1983; Saxbe & Repetti, 2010). Hence, dissatisfied couples tend to manifest morphogenic synchrony for NA and its physiological analogues, and these patterns predict future relationship dissolution and health risks (Gottman, 1994). Unfortunately, less empirical information is available about morphostatic physiological processes (coregulation) in romantic couples. Gottman (1994) noted that physiological synchrony may be a direct function of NA exchange, and may not be observable for couples that do not engage in NA reciprocity. Assuming this hypothesis, as well as those theories outlining physiological coregulation in close relationships, detection of coregulation in romantic relationships may require a physiological response not as closely tied to NA as those used in prior research. Motivated by these speculations, the current work examines coregulation of respiratory sinus arrhythmia (RSA; described below), a physiological variable central to the regulation of an emotional experience, modulated by the presence of intimate partners, and not strictly related to NA (see details below).

RSA and Close Relationships The autonomic nervous system (ANS) consists of the sympathetic and parasympathetic branches (SNS and PNS, respectively). Broadly, SNS responses correspond to states of heightened arousal needed for immediate action, whereas PNS activity occurs during moments of rest, digestion, and social attention. A relatively new (evolutionarily speaking) PNS brain region, the nucleus ambiguus, directly innervates the heart through the vagus nerve. The influence the nucleus ambiguus places on the heart can be measured through the degree of correspondence between respiration and heart rate (Berntson et al., 1997; Porges, 1995, 2007; Porges, McCabe, & Youngue, 1982; Task Force of the European Society of Cardiology and the North American Society of Pacing & Electrophysiology, 1996), commonly referred to as heart rate variability and often operationalized as RSA. The RSA pattern arises from the cardiorespiratory generator that serves as a gate keeper for vagal influence on the heart. Specifically, expiration excites vagal neurons which, in turn, exert an inhibitory effect on the heart to slow heart rate. Conversely, inspiration dampens vagal activity, effectively blocking the influence on the heart and allowing the sympathetic pacemaker to speed up heart rate (Berntson,

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HELM, SBARRA, AND FERRER

Cacioppo, & Quigley, 1993). Consequently, vagal modulation of the heart rate will manifest a fluctuation in concert with respiration; otherwise, the sympathetic pacemaker remains in control, providing a relatively linear (i.e., increasing, decreasing or flat) pattern of responsivity. In the past two decades, RSA has received much attention for its role in emotion regulation (Butler, Wilhelm, & Gross, 2006; Segerstrom & Nes, 2007), psychopathology (Beauchaine, 2001; Friedman, 2007; Katz, 2007; Thayer & Lane, 2000), health (Masi, Hawkley, Rickett, & Cacioppo, 2007), and a variety of other phenomena (for review, see Chambers & Allen, 2007). In the context of social relationships, RSA has been largely conceptualized through Porges’ polyvagal theory and Thayer’s neurovisceral integration model. Polyvagal theory integrates the evolution of the vagal system and social engagement across a variety of species (Porges, 1995, 1998, 2003). Many mammalian species are equipped with a myelinated vagal innervation that modulates heart rate, leading to the pattern of RSA in heart rate described above. According to polyvagal theory, RSA should increase as individuals engage socially with one another and feel safe within the immediate environment. In contrast, decreases in RSA occur in concert as fight-or-flight responses become more dominant (Butler et al., 2006; Porges, 1998). These expected patterns for RSA suggest that vagal activity reinforces social engagement because it enhances felt positivity and safety (Porges, 2003). Hence, extensions of polyvagal theory include mechanisms that promote courting, seduction, intimacy, and love (Porges, 1998), as well as an impetus for a secure attachment (Diamond, 2001). The neurovisceral integration model describes the relationship between heart rate variability and cognitive, affective, and autonomic regulation (Thayer, Hansen, Saus-Rose, & Johnsen, 2009). The model suggests that vagal tone (RSA at rest) indicates the individual’s level of cognitive and affective flexibility, as well as capacity to deploy physiological resources to task demands (Thayer & Lane, 2000). The myelinated vagus nerve serves to quickly modulate the heart, resulting in either a reduction in RSA to increase heart rate without a change in SNS or an increase in HRV to enhance attention to highly relevant stimuli. Those with greater vagal tone have a larger capacity to control this fluctuation, and because the channel flows through a myelinated path, the modulations occur rapidly in response to a highly dynamic environment. Therefore, those with higher vagal tone tend to show higher cognitive, attentive, and autonomic regulation, thereby promoting optimal performance complex environments. A small body of human literature supports the argument that higher quality relationships downregulate physiological stress responses and heighten PNS responsivity. For example, women cohabiting with a romantic partner exhibit higher RSA than those living alone (Horsten et al., 1999); depressed adults show higher vagal activity and lower NA when socializing with a close other (e.g., romantic partner, family member, or friend) compared with socializing with a stranger or being alone (Schwerdtfeger & Friedrich-Mai, 2009); and secure attachment predicts greater vagal tone (Diamond & Hicks, 2005). Furthermore, empirical evidence also supports a bidirectional link between PNS activity and social connectedness. For example, higher levels of vagal tone predict greater quality of relations with others (Kok & Fredrickson, 2010). In turn, consistent interactions with close companions lead to

increases in vagal tone (Holt-Lunstad, Uchino, Smith, & Hicks, 2007; Kok & Fredrickson, 2010). For men with low vagal tone, higher NA predicted lower quality of the relationship as reported by the partner (Diamond, Hicks, & Otter-Henderson, 2011). For women exhibiting high vagal tone, higher self-reported positive affect co-occurred with higher relationship satisfaction (Diamond et al., 2011). Therefore, less male NA and more positive female affect predict greater relationship quality, and both effects are moderated by RSA activity. These examples illustrate that social relationships play an important regulatory function and suggest that RSA may be one of the physiological channels through which this process is instantiated.

The Present Study Given the evidence reviewed above, we expect that high-quality relationships should promote self-regulatory capacity and that interactions with one’s partner are less physiologically taxing for people in high-quality relationships (see Diamond, 2001). Our understanding of these processes can be enhanced by studying physiological outcomes that are conceptually linked to the emotion regulatory demands of a relationship. Prior studies examining coregulation in nonadult samples have focused on heart rate (e.g., Field, 1977, 2007), a measure that reflects dual innervations from SNS and PNS (Berntson, Cacioppo, Quigley, & Fabro, 1994; Porges, 1995). Similarly, studies of NA reciprocity in romantic partners examine aggregate physiological signals that are largely sensitive to SNS functioning (Levenson & Gottman, 1983; Saxbe & Repetti, 2010). Hence, to the extent that coregulation is associated with or driven largely by PNS functioning, conducting dyadlevel assessments of RSA interdependence is an important next step in this line of work. Second, inherent in any discussion of physiological synchrony across interaction tasks is the study of time, and there is a clear need to evaluate models that capture the time-based instantiation of coregulation. As a proof-of-concept analysis, Helm, Sbarra, and Ferrer (2012) identified matching of heart rate and respiration using coupled linear oscillators, but these models are suited for signals with natural fluctuations over time. In the current report, we apply a cross-lagged panel model, a widely used model for representing time-based interdependence between two variables. Our primary aim is to evaluate the possible existence of RSA coregulation in adult romantic partners using a dyadic model that is well-suited to represent the conceptual processes of interest. We hypothesize that coregulation will manifest as covariation between partners’ responses with an overall increase in RSA, that the process will be observed as morphostatic rather than morhpogenic, and finally, that that level of dependence within the dyad will be moderated by relationship satisfaction. Higher relationship quality should correspond to a higher correlation between responses (i.e., those in higher quality relationships will show stronger coregulation, see Sbarra & Hazan, 2008).

Method Participants Thirty-two heterosexual couples were recruited as part of a larger study of dyadic interactions. Advertisements for recruitment

RSA IN CLOSE RELATIONSHIPS

were posted in the local newspaper as well as online. Both partners in the relationship were required to participate in the study. Participants varied in age (range ⫽ 18 –59 years, M ⫽ 30.26 years, SD ⫽ 11.94), length of their relationship (range ⫽ 1.5–34.8 years, M ⫽ 8.2 years, SD ⫽ 8.6 years), ethnic background (6% Asian, 74% Caucasian, 2% African American, 18% Latino), and socioeconomic status (range ⫽ ⬍$10,000 per year to ⬎$60,000 per year).

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Procedure After recruitment, participants took part in a physiological experiment consisting of several laboratory tasks. In the first, baseline task, participants were seated in comfortable armchairs spaced 3 feet apart and instructed to relax, without sleeping, for 5 min. They were asked to refrain from bodily movements, facial gestures, and vocal noises. Sleep masks were placed over their eyes and the overhead lights were turned off to elicit a calm environment. Participants sat far enough away from each other to ensure that no physical contact could be made. Research assistants asked participants to remain seated throughout the duration of the experiment and to refrain from physical contact during all tasks. Following the baseline task, participants engaged in three conversation tasks. Prior to each conversation couples selected a topic for the task. For the positive conversation, participants were instructed to choose a topic they agreed on (e.g., the first date, a future vacation). For the neutral task, participants identified a topic that reflected an open discussion (e.g., events that occurred at work that day, weather). For the negative discussion, participants selected a topic on which they disagreed (e.g., appropriate discipline for children, personal finances). A graduate student ensured that the topic was appropriate for each task. Conversations lasted 3 min, allowing each partner an opportunity to speak; one member of the couple was selected at random to talk for the first minute, the other member could talk for the second minute, and both could talk openly during the third minute. Conversations were not counterbalanced as prior research reported that negative discussions tended to contaminate conversations that followed with NA (Levenson & Gottman, 1983, p. 591).

Measures Electrocardiogram (ECG) data were collected at 1,000 samples per second from unipolar leads placed on each participant’s left and right sides and the right collar bone. Each of the unipolar leads was equipped with an Ag/AgCl spot electrode (Model 93– 010200, Mindware Technologies, Gahanna, OH). Raw ECG data were submitted to HRV 2.0 software (Model 60 –1100-00, Mindware Technologies) to calculate the interbeat interval (IBI) series for each individual. The IBI series for each individual within each task was separated into 30-s segments, or epochs, which were chosen to reliably estimate RSA for the given epoch while still providing enough data to estimate parameters from a cross-lagged panel model (detailed later). RSA was estimated for each 30-s epoch via spectral analysis of the IBI series corresponding to the epoch. These computations were performed using CMetX, a program specifically designed to estimate RSA (Allen, Chambers, & Tower, 2007). The variability of the IBI series (called heart rate variability, or HRV) represents

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the total variability of the heart for a given epoch. RSA corresponds to the portion of variability within the range of 0.12– 0.40 Hz (often referred to as high-frequency heart rate variability, or HF-HRV), as this range is common for respiration in human adults (Task Force of the European Society of Cardiology and the North American Society of Pacing & Electrophysiology, 1996). CMetX uses a 241-point finite impulse response filter with a 0.12– 0.40 band-pass to extract the level of variability and finally computes the natural log of the variability estimate to normalize the response (see Allen et al., 2007). This metric for RSA is well validated, and correlates highly with several metrics used to estimate RSA. Respiration was collected at 1,000 samples per second via an elastic chest belt (TSD201, BIOPAC Systems, Santa Barbara, CA). The transducer within the elastic belt measured changes in circumference of the participant’s chest that corresponded to breathing. Following attachment of the respiration belt, experimenters asked participants if the belt felt uncomfortable or affected normal breathing. If a participant reported any effect of the belt on breathing the experimenter reattached the device until the experimenter achieved a nondisruptive attachment with good respiration measurement. The respiration belt was connected to an amplifier (RSP100C, BIOPAC Systems) that had a 1.0-Hz low-pass filter and a 0.05-Hz high-pass filter, with a gain of 20. Recorded respiration data were sent online to a computer for display (AcqKnowledge, BIOPAC Systems) and storage. Respiration rate was estimated for each 30-s epoch by counting the number of inhalations within the interval of interest. Respiration rate was required because estimates of RSA can be biased by respiration rate (Grossman, Karemaker, & Wieling, 1991). To account for this dependence, we included respiration rate as a covariate in our statistical analyses. Prior to physiological measurement, participants completed a subset of the Perceived Relationship Quality Components Inventory (PRQC), a questionnaire designed to measure perceived relationship quality (Fletcher, Simpson, & Thomas, 2000). The questionnaire contained six items to which participants responded using a 7-point Likert scale. Each item corresponded to one of six different underlying dimensions of relationship quality (satisfaction, commitment, intimacy, trust, passion, and love). Examples items include “How satisfied are you with your relationship” and “How committed are you to your relationship.” The PRQC has been shown to be negatively related to attachment avoidance and anxiety and unrelated to dimensions of personality (Noftle & Shaver, 2006). The PRQC produced high levels of internal consistency (␣ ⫽ .85). A composite was used as a measure of relationship satisfaction for each individual.

Cross-Lagged Panel Models for Assessing Coregulation For the current study we operationalized coregulation as significant interdependence between dyad members within an optimal bound. To examine such an interdependence, we implemented the following model: RSAmt ⫽ ␤0m ⫹ ␤1mRSAmt⫺1 ⫹ ␤2mRSA f t⫺1 ⫹ ␤3mRRmt ⫹ εmt RSA f t ⫽ ␤0f ⫹ ␤1f RSA f t⫺1 ⫹ ␤2f RSAmt⫺1 ⫹ ␤3f RR f t ⫹ ε f t

(1)

In this bivariate model, RSA for each member of the dyad (male

HELM, SBARRA, AND FERRER

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m, and female f) at any given time t is expressed as a function of the average RSA score throughout the series (␤0), the RSA score at the previous occasion t - 1 (␤1; autoregressive parameter), the partner’s previous RSA score (␤2; cross-partner parameter), and respiration rate at a concurrent time (␤3). Concurrent respiration rate (RR) may influence levels of RSA (Grossman et al., 1991), hence ␤3 accounts for dependence between current RSA and RR to prevent inflated estimates of ␤0m – ␤2f. Using this specification, estimates for ␤2m and ␤2f indicate coregulation because they represent the degree of lagged dependence in RSA beyond one’s own RSA response (i.e., beyond the effect of ␤1m and ␤1f).1 All models were fitted using SAS PROC MIXED (SAS Institute Inc., 2011), which accounts for the within- and between-couple variability in the outcome variable (Thiébaut, Jacqmin-Gadda, Chene, Leport, & Commenges, 2002).

Results Testing the Presence of RSA Coregulation Coregulation was defined as dependence between partners’ physiological responses within an optimal bound. In the context of the model presented in Equation 1, the existence of coregulation implies that ␤0 and ␤2 (for both men and women) will be larger in the conversation tasks, indicating both an increase in average RSA and a greater dependence between partners in those tasks. A series of nested models was constructed to test these hypotheses. The comparison of model fit for each of these models is available in Table 1. Models 1–3 tested for mean differences in average RSA from baseline to conversation, Models 4 and 5 tested for differences in the autoregressive and cross-partner effects between baseline and conversation tasks, and Models 6 and 7 tested for gender differences for the autoregressive and cross-partner effects. All models were fitted using maximum likelihood to make nested statistical comparisons via ␹2 difference testing. The first three models tested an increase in RSA for men and women from the baseline to the conversation tasks. The first model, serving as a “null model,” constrained ␤0 across tasks, ␤1 across gender and tasks, and ␤2 across gender and tasks. This model implies that there is no difference in RSA responses between the baseline and social interaction tasks. The second model freed ␤0 across tasks for men, testing for a mean difference in RSA

Table 1 Comparisons of Model Fit for Different Tests of Coregulation Model

⫺2 LL

df

⌬-2 LL

p

1 2 3 4 5 6 7

4393.8 4387.3 4387.3 4377.1 4373.1 4368.1 4368.1

7 8 9 10 11 12 13

— 6.5 0.0 10.2 4.0 5.0 0.0

— 0.01 1.00 ⬍0.01 0.04 0.02 1.00

Note. Models 1–7 are described in the text. ⫺2 LL ⫽ the models ⫺2 log-likelihood; df ⫽ the number of parameters estimated in the model; ⌬-2 LL ⫽ the difference between the prior and current model; p ⫽ the probability of ⌬-2 LL, with p ⬍ .05 indicating better fit of the model with more parameters; df ⫽ degrees of freedom.

between resting and conversation tasks. This model fit significantly better than the first one (⌬␹2/⌬df ⫽ 6.5/1, p ⫽ .01). The third model freely estimated ␤0 across tasks for the women, but did not improve the fit (⌬␹2/⌬df ⫽ 0.0, p ⫽ 1.0). The coefficients in Model 2 indicate that the men increased in average RSA during the conversation tasks (␤0 ⫽ 0.25, p ⫽ .01). Therefore, the results indicate a significant change in RSA response for men but not women. Models 4 and 5 tested for changes in the autoregressive and cross-partner effects between tasks. In Model 4, the ␤1 parameter was freely estimated across tasks, testing for change in the autoregressive effect across the tasks. This model fit better than Model 3 (⌬␹2/⌬df ⫽ 10.2/1, p ⬍ .01). In the fifth model, ␤2 was freely estimated across tasks, testing for differences in cross-partner association between the tasks. Model 5 fit better than Model 4 (⌬␹2/⌬df ⫽ 4.0/1, p ⫽ .04). According to the coefficients from Model 5, participants had lower levels of autoregression (␤1 ⫽ ⫺.15, p ⬍ .01) and higher cross-partner effects (␤2 ⫽ .08, p ⫽ .04) during the conversation tasks. Taken together these results suggest that individuals’ RSA were less tightly associated with their own levels and more related to their partners’ RSA during the conversation tasks, compared with the resting task. Models 6 and 7 tested whether the changes in the autoregressive and cross-partner effects were different for men and women. Model 6 freely estimated changes in the ␤1 for men and women, and Model 7 freely estimated changes in the ␤2 parameter for men and women. Model 6 showed better fit that Model 5, but Model 7 did not improve the fit from Model 6 (⌬␹2/⌬df ⫽ 5.0/1, p ⫽ .02; ⌬␹2/⌬df ⫽ 0.0/1, p ⫽ 1.0, respectively). According to Model 6, women showed a slightly lower autoregressive effect (␤1f ⫽ ⫺.09, p ⫽ .07) and men a significantly lower autoregressive effect (␤1m ⫽ ⫺.13, p ⫽ .02) during the conversation tasks. These results imply that men have less stability in RSA than women during conversation tasks, but both have similar cross-partner effects. Given these model comparisons, the most parsimonious model that still adequately summarizes the data is Model 6. This model suggests a significant increase in men’s average RSA, a slight decrease in stability for women’s RSA, a larger decrease in stability for men’s RSA, and a significant increase in between-partner correlation of RSA from the baseline to the conversation tasks. Table 2 presents the parameter estimates and standard errors from this model. A conceptual depiction of these results is given in Figure 1, and examples of data from two couples are provided in Figure 2. Two extra models were used to test whether the cross-partner effects changed across the different conversation tasks (positive, neutral, and negative) and across the conversation role (talking, listening, and freely talking). However, the models fitted previously (i.e., Models 1–7) are not nested within these two new models. Therefore, statistical fit cannot be compared between them, and they are not included in Table 1. The first model tested if the cross-partner effect changed during different conversation tasks (-2LL ⫽ 4367.4), and the second model investigated differ1 These models can also be fitted as structural equation models, which can include a residual covariance. We did not estimate this parameter for the current model within the mixed effects framework, although a test showed that estimating this residual covariance within a structural equation model had virtually no effect on the parameter estimates.

RSA IN CLOSE RELATIONSHIPS

Table 2 Coefficients From Best-Fitting Cross-Lagged Panel Model (Model 6) Task Baseline

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Conversation

Coefficient

Estimate

SE

t

p

␤0m ␤1m ␤2m ␤0f ␤1f ␤2f

6.30 .62 ⫺.01 6.50 .62 ⫺.01

.12 .03 .03 .13 .03 .03

50.42 18.88 ⫺.08 50.49 18.88 ⫺.08

⬍.01 ⬍.01 .93 ⬍.01 ⬍.01 .93

␤0m ␤1m ␤2m ␤0f ␤1f ␤2f

6.50 .40 .09 6.56 .53 .09

.12 .04 .03 .12 .04 .03

52.31 9.09 2.87 53.30 13.24 2.87

⬍.01 ⬍.01 ⬍.01 ⬍.01 ⬍.01 ⬍.01

Note. ␤0 ⫽ the average RSA throughout the task; ␤1 ⫽ the degree of autocorrelation; ␤2 quantifies the level of cross-partner correlation; subscripts m and f ⫽ male and female, respectively.

ences between talking and listening (-2LL ⫽ 4370.0). Based on these results, we conclude that Model 6 remains the best model for the data because the first model gives a difference of 0.6 for three extra parameters (which would be highly nonsignificant if the models were nested), and the second fits the data slightly worse. Therefore, we do not have sufficient evidence from the data to conclude that the cross-lagged effects change across tasks or between listening and talking in the conversations.

Morphostatic Versus Morphogenic Processes Butler and Randall (2013) suggested that coregulation manifests as a morphostatic process rather than as a morphogenic process. Following this proposition, average RSA should not change over time. To test this hypothesis, we conducted a repeated-measures analysis of variance to examine whether average RSA differs between any of the measurement occasions throughout the conversation tasks. This is a very general method for testing whether the data show mean differences between any pair of measurement occasions (i.e., within or between the discussion tasks). If a mean difference did exist, then we could include predictors for the factors or levels when this occurred (i.e., task, listening). The analysis did not reveal differences between time points, F(17, 440) ⫽ .74, p ⫽ .75, gender, F(1,28) ⫽ 1.35, p ⫽ .25, or their interaction, F(17, 439) ⫽ .50, p ⫽ .95. Therefore the data do not exhibit significant differences in average response during any of the measurement occasions. This analysis indicates that RSA follows a flat trajectory throughout the conversation tasks for both men and women, corresponding to a morphostatic process. Furthermore, because the repeated-measures analysis of variance did not detect any mean change, including other factors to predict RSA would also produce nonsignificant effects. Hence we conclude that RSA follows a morphostatic process.

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include a moderating effect of relationship satisfaction. To do this, we first created a variable defined as the product of the partners’ past RSA and relationship satisfaction and then added the new term to Model 6 (its effect was labeled ␤4). Results from these analyses indicate that partners’ levels of relationship satisfaction moderated the cross-partner effect (␤4 ⫽ .08, p ⬍ .01). An alternative model specification that that allowed the effect to vary across gender did not improve the fit (␹2/⌬df ⫽ .2, df ⫽ 1, p ⫽ .65). Accordingly, this pattern of effects suggests that those participants with high relationship satisfaction, irrespective of their gender, showed a stronger link between their RSA levels on a given occasion and their partners’ RSA at the next occasion.

Discussion One way in which close relationships may exert positive effects on health is through social emotion regulation at the level of physiology (Beckes & Coan, 2011; Coan, 2008; Sbarra & Hazan, 2008). In the current report, we operationalized this process as the coregulation of RSA among two members of a romantic couple. We tested the existence of physiological coregulation, change in RSA as morphostatic versus morphogenic, and the moderating effects of relationship satisfaction. Compared with resting levels, men in our sample evidenced a significant increase in RSA when engaged in discussion with their romantic partner, but such change was not found for women. During the interaction tasks (but not baseline) we observed positive cross-partner effects for RSA, indicating that when one partner had high levels of RSA at a given occasion, the other partner had reliably higher RSA at the following occasion. Relationship satisfaction moderated this dependence—people reporting a higher quality relationship, regardless of their gender, showed significantly stronger RSA synchrony. Finally, no differences were apparent for RSA during the conversation tasks, suggesting that RSA manifests, at least in this study, as a morphostatic rather than a morphogenic process. Our findings are largely consistent with the perspective on physiological coregulation proposed by Butler and Randall (2013). Specifically, they hypothesized a morphostatic process characterized by a bidirectional link of physiological responding within optimal bounds. The prescribed bidirectional link matches the

B

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RSA ♀, t

time

Relationship Satisfaction as a Moderator In the last set of analyses, we examined the role of relationship satisfaction as a potential moderator of between-couple differences in coregulation. These analyses required extending Model 6 to

Figure 1. Pictorial representations of RSA dynamics for the baseline and conversation tasks. (A) Summary of the baseline task; (B) characterization of the conversation tasks. Superscripts for the coefficients have the following definitions: ⴱ p ⬍ .05, ns p ⬎ .05.

HELM, SBARRA, AND FERRER

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Conversation

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Figure 2. RSA time series plots for couples 672 (A) and 678 (B) during the resting (first column) and conversation (second column) tasks. Solid lines represent RSA responses from each partner across repeated measures. Dashed lines in the conversation plots indicate transition points for the topic of conversation (positive ¡ neutral ¡ negative). Labels on the x-axis correspond to levels of RSA, and labels on the y-axis indicate time in minutes.

significant cross-partner effects found for both men and women observed in the current study, and the optimal bound corresponds to the increase in average RSA for men. Furthermore, the data follow a morphostatic process because there was no detectable trend in RSA over time. The current findings also extend prior knowledge about physiological coregulation between close adult partners. Prior work reported greater correlations in physiological arousal for couples with lower relationship quality (e.g., Levenson & Gottman, 1983; Saxbe & Repetti, 2010). Here, we observed a different pattern of results: greater dependence for couples with higher relationship satisfaction. These effects align with our hypothesis that physiological measures not directly related to NA may be the best channels to assess the physiological instantiation of coregulation. Although the current results cannot confirm this postulate unequivocally, we offer it as question for future empirical inquiry. Is it possible that the physiological instantiation of coregulation operates mainly to enhance PNS functioning rather than to dampen SNS functioning? This possibility may help explain some of the conceptual confusion in the literature between stress buffering and coregulation (see Sbarra & Hazan, 2008). An important direction for this line of research involves the examination of physiological indicators of stress versus relaxation and restoration. Given that prior work has emphasized indicators of stress (Levenson & Gottman, 1983; Saxbe & Repetti, 2010) and the current work examines signals typically associated with restoration and the capacity to deploy physiological resources as needed

(Thayer & Lane, 2000; Thayer et al., 2009), an important difference arises. For example, one might expect that couples with low relationship satisfaction would share physiological responses generated by the SNS patterns of blood pressure, preejection period, or skin conductance. In contrast, couples with high relationship satisfaction may show dependence for respiratory sinus arrhythmia or other signals associated with parasympathetic response. Therefore, the expectation of physiological correspondence between partners may manifest through the kind of response (parasympathetic vs. sympathetic) and the quality of the relationship. We expected to observe increases in RSA for both men and women during social interaction tasks. Contrary to our expectations, this effect was only detectable for men. One possible explanation for this difference is that RSA constitutes an important mechanism driving the observed sex differences associated with romantic involvement for men and women (Kiecolt-Glaser & Newton, 2001). Although women are more aware of and affected by marital quality (e.g., spending more time thinking about the relationship and have more detailed accounts of disagreements), men suffer more adverse health outcomes when relationships end (Sbarra et al., 2011; Stroebe, Schut, & Stroebe, 2007). Because high levels of RSA are associated with decreased risk for morbidity and mortality (Masi et al., 2007; Thayer & Lane, 2007; Thayer & Sternberg, 2006), it is possible that men incur greater risk due to the loss of coregulatory processes conferred by their relationship. To the extent that men are at significantly increased risk for poor

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RSA IN CLOSE RELATIONSHIPS

health outcomes following relationship loss, one explanation for this effect may be the loss of increases in RSA as a function of interaction with their partner. The other side of the same coin, of course, is that men may benefit from intact marriage more than women, and here we see one physiological route—increased HRV—through which this process may unfold. Although the exact reason for RSA increases in men remains unclear in the current study, we hope the findings motivates additional work seeking to understand potential sex differences in RSA during social interactions.2 We also note some limitations of the current study. For example, it is possible that cross-partner effects differ across the discussion tasks (i.e., positive vs. neutral vs. negative), but the current sample size was too small to reliably detect those differences. Replication of the current work with a larger sample size can help determine if differences exist. Furthermore, several variables that might have contributed to the observed cross-lagged partner effects (i.e., tone, facial expression) were not collected during the conversations. Although we believe these effects are largely due to coregulation, other unmeasured variables may partly underlie the observed effects. Again, replication with measurement and corresponding analysis of these other variables can help to elucidate the true effect of coregulation in romantic couples.3

Conclusions In this article, we evaluated the covariation of RSA in 32 romantic couples during a series of laboratory tasks using a crosslagged panel model. During the conversation tasks, men’s and women’s RSA were associated with their partners’ previous RSA responses, and this pattern was stronger for those couples with higher relationship satisfaction. This pattern of responses follows Butler and Randall’s (2013) specification of coregulation, with significant correlation between RSA within an optimal bound. Relationship satisfaction also plays an important role in the current investigation, indicating that greater relationship satisfaction enhances the coregulation of RSA within couples. This contrasts with other findings (Levenson & Gottman, 1983; Saxbe & Repetti, 2010), suggesting that coregulation may occur via PNS channels. Finally, the observed increases in RSA for men, but not women, suggests a possible mechanistic route by which men may accrue larger health-relevant benefits of their involvement in romantic relationships.

2 A follow-up analysis showed that higher female relationship satisfaction predicted higher male RSA during the conversation tasks (b ⫽ .67, p ⬍ .01), but men’s relationship satisfaction did not predict women’s RSA. Although it is not clear if higher coregulation actually leads to greater RSA, we do note that, at least for men, relationship satisfaction predicts higher cross-association of RSA as well as increases in RSA. 3 We thank an anonymous reviewer for noting these possibilities.

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Received October 9, 2012 Revision received October 28, 2013 Accepted November 12, 2013 䡲